import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, activations

base_model = keras.applications.mobilenet.MobileNet(
    input_shape=None,
    include_top=False,
    weights='imagenet',
    pooling='avg',
    classes=1000
)
base_model.trainable = False

customer_model = layers.Dense(3)

model = keras.Sequential([
    base_model,
    customer_model,
])
model.summary()

x = tf.ones((4, 224, 224, 3), dtype=tf.float32)
pred = model(x)
print(pred.shape)

x = tf.ones((4, 448, 448, 3), dtype=tf.float32)
pred = model(x)
print(pred.shape)
